Development of a Deep Neural Network-based Life Accident Evaluation Model for Weather-related Railway Accidents

Global warming worldwide is the reason for the increasing number of meteorological disasters causing severe property damage and human casualties. Railways are a key social infrastructure; however, quantitative and empirical research into the impact of weather changes due to global warming has not been done adequately. Thus, this study aims to develop a predictive model using a deep learning algorithm to quantify the relationship between fatal rail accidents and weather conditions. The proposed framework utilizes the Deep Neural Network (DNN) technique trained with past rail accidents and weather data. The model performance was evaluated using error metrics (mean absolute error (MAE) and root-mean-square error (RMSE)) and compared with widely used regression techniques. The findings showed that the DNN model achieved lower RMSE and MAE compared to the multi-regression, random forest and support vector machine models, with a reduction in prediction error ranging from 1.04% to 20.78% in RMSE and 5.0% to 15.3% in MAE. This exhibits the DNN model's effectiveness in capturing complex relationships within the data and delivering more accurate predictions compared to the other models. The approach and outcomes of this study provide essential guidelines for the efficient and safe maintenance and optimized safety management of railway services.